Decision Trees
Understanding decision trees and their implementation for classification and regression
What are Decision Trees?
A versatile machine learning algorithm for classification and regression tasks
Decision trees are a popular supervised learning method used for both classification and regression tasks. They work by creating a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
How Decision Trees Work
A decision tree is a flowchart-like structure where:
- Internal nodes represent a "test" on an attribute (e.g., whether a feature is greater than a certain value)
- Branches represent the outcome of the test
- Leaf nodes represent class labels or continuous values (for classification or regression)
Decision Tree Learning Process
- Start at the root node with the entire dataset
- Find the best feature and threshold to split the data that maximizes information gain
- Create child nodes based on the split
- Recursively repeat the process for each child node until stopping criteria are met
- Assign class labels or values to the leaf nodes
Splitting Criteria
Decision trees use different metrics to determine the best split:
- Gini Impurity: Measures the probability of incorrect classification
- Entropy: Measures the level of disorder or uncertainty
- Information Gain: The reduction in entropy after a dataset is split
- Mean Squared Error: Used for regression trees to minimize prediction error
Advantages and Limitations
Advantages
- Easy to understand and interpret
- Requires little data preprocessing
- Can handle both numerical and categorical data
- Can handle multi-output problems
- Implicitly performs feature selection
- Non-parametric (no assumptions about data distribution)
Limitations
- Can create overly complex trees that don't generalize well
- Prone to overfitting, especially with deep trees
- Can be unstable (small variations in data can result in different trees)
- Biased toward features with more levels
- Not optimal for continuous variables
- May struggle with imbalanced datasets